Magnetic fields create eddy currents within certain types of materials in objects in their path. The eddy currents in turn affect the magnetic field as observed from outside the objects. Cracks, discontinuities, holes, and changes in the material content all affect the eddy current flow within an object and also affect the magnetic field external to the object. Accordingly, magnetic fields can be used to scan materials to determine if the materials contain inconsistencies and anomalies (such as cracks or corrosion) that affect the magnetic field.
Remote-field eddy-current techniques can be used to scan materials. Remote-field eddy-current techniques (RFEC) generally involve detecting magnetic-field changes caused by anomalies on a surface of, and/or hidden in, a structure due to the RFEC technology's double-wall-transmission feature, while near-field eddy-current techniques generally involve detecting magnetic-field changes caused by anomalies on surface and near-surface areas due to the direct coupling of excitation unit(s) and sensor unit(s). Generally, the drive-sensor separation for an RFEC probe is greater than that of a non-RFEC probe; however, geometrical separation of the two coils is not a defining characteristic that distinguishes a “remote field” from a “near field” probe. Changes to an observed RFEC signal can be caused by undesirable anomalies, such as cracks, voids, internal or surface corrosion, embedded foreign objects, alloy-composition changes, etc., as well as by expected inherent features of the object being examined, such as joints and fasteners.
Users desire probes and techniques that are fast, reliable, accurate, easy to operate, and inexpensive. There is a need to extend the RFEC technique, as well as other eddy-current techniques for better noise control and small-flaw detection for inspection of various objects with different geometries, for example, those with flat geometry, or with approximately flat geometry in at least a local area, as well as objects with other surface geometries. In particular, there is a need to improve and/or automate detection of undesirable anomalies that are near expected inherent features of an object.